OpenRaiser/Intern-Atlas huggingface dataset:
Towards Decentralized Identity Management in Multi-stakeholder 6G Networks
Venue: arXiv cs.NI 2022
Year: 2022

Trust-building mechanisms among network entities of different administrative domains will gain significant importance in 6G because a future mobile network will be operated cooperatively by a variety of different stakeholders rather than by a single mobile network operator. The use of trusted third party issued certificates for initial trust establishment in multi-stakeholder 6G networks is only advisable to a limited extent, as trusted third parties not only represent single point of failures or attacks, but they also cannot guarantee global independence due to national legislation and regulatory or political influence. This article proposes to decentralize identity management in 6G networks to enable secure mutual authentication between network entities of different trust domains without relying on a trusted third party and to empower network entities with the ability to shape and strengthen cross-domain trust relationships by the exchange of verifiable credentials. A reference model for decentralized identity management in 6G is given as an initial guide for the fundamental design of a common identity management system whose operation and governance are distributed equally across multiple trust domains of interconnected and multi-stakeholder 6G ecosystems.
First do not fall: learning to exploit a wall with a damaged humanoid robot
Venue: arXiv cs.RO 2022
Year: 2022

Humanoid robots could replace humans in hazardous situations but most of such situations are equally dangerous for them, which means that they have a high chance of being damaged and falling. We hypothesize that humanoid robots would be mostly used in buildings, which makes them likely to be close to a wall. To avoid a fall, they can therefore lean on the closest wall, as a human would do, provided that they find in a few milliseconds where to put the hand(s). This article introduces a method, called D-Reflex, that learns a neural network that chooses this contact position given the wall orientation, the wall distance, and the posture of the robot. This contact position is then used by a whole-body controller to reach a stable posture. We show that D-Reflex allows a simulated TALOS robot (1.75m, 100kg, 30 degrees of freedom) to avoid more than 75% of the avoidable falls and can work on the real robot.
Results Merging in the Patent Domain
Venue: arXiv cs.IR 2022
Year: 2022

In this paper, we test machine learning methods for results merging in patent document retrieval. Specifically, we examine random forest, decision tree, support vector machine (SVR), linear regression, polynomial regression, and deep neural networks (DNNs). We use two different methods for results merging, the multiple models (MM) method and the global model method (GM). Furthermore, we examine whether the ranking of the document's scores is linearly explainable. The CLEF-IP 2011 standard test collection was used in our experiments. The random forest produces the best results in comparison to all other models, and it fits the data better than linear and polynomial approaches.
Popularity Bias in Collaborative Filtering-Based Multimedia Recommender Systems
Venue: arXiv cs.IR 2022
Year: 2022

Multimedia recommender systems suggest media items, e.g., songs, (digital) books and movies, to users by utilizing concepts of traditional recommender systems such as collaborative filtering. In this paper, we investigate a potential issue of such collaborative-filtering based multimedia recommender systems, namely popularity bias that leads to the underrepresentation of unpopular items in the recommendation lists. Therefore, we study four multimedia datasets, i.e., LastFm, MovieLens, BookCrossing and MyAnimeList, that we each split into three user groups differing in their inclination to popularity, i.e., LowPop, MedPop and HighPop. Using these user groups, we evaluate four collaborative filtering-based algorithms with respect to popularity bias on the item and the user level. Our findings are three-fold: firstly, we show that users with little interest into popular items tend to have large user profiles and thus, are important data sources for multimedia recommender systems. Secondly, we find that popular items are recommended more frequently than unpopular ones. Thirdly, we find that users with little interest into popular items receive significantly worse recommendations than users with medium or high interest into popularity.
Addressing Randomness in Evaluation Protocols for Out-of-Distribution Detection
Venue: arXiv cs.LG 2022
Year: 2022

Deep Neural Networks for classification behave unpredictably when confronted with inputs not stemming from the training distribution. This motivates out-of-distribution detection (OOD) mechanisms. The usual lack of prior information on out-of-distribution data renders the performance estimation of detection approaches on unseen data difficult. Several contemporary evaluation protocols are based on open set simulations, which average the performance over up to five synthetic random splits of a dataset into in- and out-of-distribution samples. However, the number of possible splits may be much larger, and the performance of Deep Neural Networks is known to fluctuate significantly depending on different sources of random variation. We empirically demonstrate that current protocols may fail to provide reliable estimates of the expected performance of OOD methods. By casting this evaluation as a random process, we generalize the concept of open set simulations and propose to estimate the performance of OOD methods using a Monte Carlo approach that addresses the randomness.
Nonlinear Kernel Support Vector Machine with 0-1 Soft Margin Loss
Venue: arXiv cs.LG 2022
Year: 2022

Recent advance on linear support vector machine with the 0-1 soft margin loss ($L_{0/1}$-SVM) shows that the 0-1 loss problem can be solved directly. However, its theoretical and algorithmic requirements restrict us extending the linear solving framework to its nonlinear kernel form directly, the absence of explicit expression of Lagrangian dual function of $L_{0/1}$-SVM is one big deficiency among of them. In this paper, by applying the nonparametric representation theorem, we propose a nonlinear model for support vector machine with 0-1 soft margin loss, called $L_{0/1}$-KSVM, which cunningly involves the kernel technique into it and more importantly, follows the success on systematically solving its linear task. Its optimal condition is explored theoretically and a working set selection alternating direction method of multipliers (ADMM) algorithm is introduced to acquire its numerical solution. Moreover, we firstly present a closed-form definition to the support vector (SV) of $L_{0/1}$-KSVM. Theoretically, we prove that all SVs of $L_{0/1}$-KSVM are only located on the parallel decision surfaces. The experiment part also shows that $L_{0/1}$-KSVM has much fewer SVs, simultaneously with a decent predicting accuracy, when comparing to its linear peer $L_{0/1}$-SVM and the other six nonlinear benchmark SVM classifiers.
Towards Practices for Human-Centered Machine Learning
Venue: arXiv cs.LG 2022
Year: 2022

"Human-centered machine learning" (HCML) is a term that describes machine learning that applies to human-focused problems. Although this idea is noteworthy and generates scholarly excitement, scholars and practitioners have struggled to clearly define and implement HCML in computer science. This article proposes practices for human-centered machine learning, an area where studying and designing for social, cultural, and ethical implications are just as important as technical advances in ML. These practices bridge between interdisciplinary perspectives of HCI, AI, and sociotechnical fields, as well as ongoing discourse on this new area. The five practices include ensuring HCML is the appropriate solution space for a problem; conceptualizing problem statements as position statements; moving beyond interaction models to define the human; legitimizing domain contributions; and anticipating sociotechnical failure. I conclude by suggesting how these practices might be implemented in research and practice.
Bridge the Gap between Supervised and Unsupervised Learning for Fine-Grained Classification
Venue: arXiv cs.CV 2022
Year: 2022

Unsupervised learning technology has caught up with or even surpassed supervised learning technology in general object classification (GOC) and person re-identification (re-ID). However, it is found that the unsupervised learning of fine-grained visual classification (FGVC) is more challenging than GOC and person re-ID. In order to bridge the gap between unsupervised and supervised learning for FGVC, we investigate the essential factors (including feature extraction, clustering, and contrastive learning) for the performance gap between supervised and unsupervised FGVC. Furthermore, we propose a simple, effective, and practical method, termed as UFCL, to alleviate the gap. Three key issues are concerned and improved: First, we introduce a robust and powerful backbone, ResNet50-IBN, which has an ability of domain adaptation when we transfer ImageNet pre-trained models to FGVC tasks. Next, we propose to introduce HDBSCAN instead of DBSCAN to do clustering, which can generate better clusters for adjacent categories with fewer hyper-parameters. Finally, we propose a weighted feature agent and its updating mechanism to do contrastive learning by using the pseudo labels with inevitable noise, which can improve the optimization process of learning the parameters of the network. The effectiveness of our UFCL is verified on CUB-200-2011, Oxford-Flowers, Oxford-Pets, Stanford-Dogs, Stanford-Cars and FGVC-Aircraft datasets. Under the unsupervised FGVC setting, we achieve state-of-the-art results, and analyze the key factors and the important parameters to provide a practical guidance.
A Survey on How Test Flakiness Affects Developers and What Support They Need To Address It
Venue: arXiv cs.SE 2022
Year: 2022

Non-deterministically passing and failing test cases, so-called flaky tests, have recently become a focus area of software engineering research. While this research focus has been met with some enthusiastic endorsement from industry, prior work nevertheless mostly studied flakiness using a code-centric approach by mining software repositories. What data extracted from software repositories cannot tell us, however, is how developers perceive flakiness: How prevalent is test flakiness in developers' daily routine, how does it affect them, and most importantly: What do they want us researchers to do about it? To answer these questions, we surveyed 335 professional software developers and testers in different domains. The survey respondents confirm that flaky tests are a common and serious problem, thus reinforcing ongoing research on flaky test detection. Developers are less worried about the computational costs caused by re-running tests and more about the loss of trust in the test outcomes. Therefore, they would like to have IDE plugins to detect flaky code as well as better visualizations of the problem, particularly dashboards showing test outcomes over time; they also wish for more training and information on flakiness. These important aspects will require the attention of researchers as well as tool developers.
Emergence of human oculomotor behavior from optimal control of a cable-driven biomimetic robotic eye
Venue: arXiv cs.RO 2022
Year: 2022

In human-robot interactions, eye movements play an important role in non-verbal communication. However, controlling the motions of a robotic eye that display similar performance as the human oculomotor system is still a major challenge. In this paper, we study how to control a realistic model of the human eye with a cable-driven actuation system that mimics the six degrees of freedom of the extra-ocular muscles. The biomimetic design introduces novel challenges to address, most notably the need to control the pretension on each individual muscle to prevent the loss of tension during motion, that would lead to cable slack and lack of control. We built a robotic prototype and developed a nonlinear simulator and two controllers. In the first approach, we linearized the nonlinear model, using a local derivative technique, and designed linear-quadratic optimal controllers to optimize a cost function that accounts for accuracy, energy expenditure, and movement duration. The second method uses a recurrent neural network that learns the nonlinear system dynamics from sample trajectories of the system, and a non-linear trajectory optimization solver that minimizes a similar cost function. We focused on the generation of rapid saccadic eye movements with fully unconstrained kinematics, and the generation of control signals for the six cables that simultaneously satisfied several dynamic optimization criteria. The model faithfully mimics the three-dimensional rotational kinematics and dynamics observed for human saccades. Our experimental results indicate that while both methods yielded similar results, the nonlinear method is more flexible for future improvements to the model, for which the calculations of the linearized model's position-dependent pretensions and local derivatives become particularly tedious.
Keeping Fun Alive: an Experience Report on Running Online Coding Camps
Venue: arXiv cs.SE 2022
Year: 2022

The outbreak of the COVID-19 pandemic prohibited radically the collocation and face-to-face interactions of participants in coding bootcamps and similar experiences, which are key characteristics that help participants to advance technical work. Several specific issues are faced and need to be solved when running online coding camps, which can achieve the same level of positive outcomes for participants. One of such issues is how to keep the same level of fun that participants obtained through physical activities and interactions in the face-to-face settings. In this paper, we report on our experience and insights gained from designing and running a fully remote coding camp that exposes high school students to Agile-based Software Engineering practices to enhance their ability to develop high-quality software. To design the online coding camp, we adapted the face-to-face version of the coding camp to keep the same "level of fun", i.e., adaptations aimed at increasing communication, engaging participants, and introducing fun items to reduce fatigue due to prolonged computer use, while preserving the technical curriculum that enables students to attain the learning goals originally planned. The comparison with the results of the face-to-face coding camp shows that we succeeded in keeping the fun alive in the online edition, and the participants of online camp were able to produce the results at the same level of quality in terms of product and process as in the face-to-face edition. From our experience, we synthesize lessons learned, and we sketch some guidelines for educators.
Reconfigurable Intelligent Surface-Aided Spectrum Sharing Coexisting with Multiple Primary Networks
Venue: arXiv cs.IT 2022
Year: 2022

Considering the spectrum sharing system (SSS) coexisting with multiple primary networks, we have employed a well-designed reconfigurable intelligent surface (RIS) to control the radio environments of wireless channels and relieve the scarcity of the spectrum resource in this work. Specifically, the enhancement of the spectral efficiency of the secondary user in the considered SSS is decomposed into two subproblems which are a second-order cone programming (SOCP) and a fractional programming of the convex quadratic form (CQFP), respectively, to optimize alternatively the beamforming vector at the secondary access point (S-AP) and the reflecting coefficients at the RIS. The SOCP subproblem is shown as a concave problem, which can be solved optimally using standard convex optimization tools. The CQFP subproblem can be solved by a low-complexity method of gradient-based linearization with domain (GLD), providing a sub-optimal solution for fast deployment. Taking the discrete phase control at the RIS into account, a nearest point searching with penalty (NPSP) method is also developed, realizing the discretization of the phase shifts of the RIS in practice. The simulation results indicate that both GLD and NPSP can achieve an excellent performance.
E-LMC: Extended Linear Model of Coregionalization for Spatial Field Prediction
Venue: arXiv cs.LG 2022
Year: 2022

Physical simulations based on partial differential equations typically generate spatial fields results, which are utilized to calculate specific properties of a system for engineering design and optimization. Due to the intensive computational burden of the simulations, a surrogate model mapping the low-dimensional inputs to the spatial fields are commonly built based on a relatively small dataset. To resolve the challenge of predicting the whole spatial field, the popular linear model of coregionalization (LMC) can disentangle complicated correlations within the high-dimensional spatial field outputs and deliver accurate predictions. However, LMC fails if the spatial field cannot be well approximated by a linear combination of base functions with latent processes. In this paper, we present the Extended Linear Model of Coregionalization (E-LMC) by introducing an invertible neural network to linearize the highly complex and nonlinear spatial fields so that the LMC can easily generalize to nonlinear problems while preserving the traceability and scalability. Several real-world applications demonstrate that E-LMC can exploit spatial correlations effectively, showing a maximum improvement of about 40% over the original LMC and outperforming the other state-of-the-art spatial field models.
DynamicRetriever: A Pre-training Model-based IR System with Neither Sparse nor Dense Index
Venue: arXiv cs.IR 2022
Year: 2022

Web search provides a promising way for people to obtain information and has been extensively studied. With the surgence of deep learning and large-scale pre-training techniques, various neural information retrieval models are proposed and they have demonstrated the power for improving search (especially, the ranking) quality. All these existing search methods follow a common paradigm, i.e. index-retrieve-rerank, where they first build an index of all documents based on document terms (i.e., sparse inverted index) or representation vectors (i.e., dense vector index), then retrieve and rerank retrieved documents based on similarity between the query and documents via ranking models. In this paper, we explore a new paradigm of information retrieval with neither sparse nor dense index but only a model. Specifically, we propose a pre-training model-based IR system called DynamicRetriever. As for this system, the training stage embeds the token-level and document-level information (especially, document identifiers) of the corpus into the model parameters, then the inference stage directly generates document identifiers for a given query. Compared with existing search methods, the model-based IR system has two advantages: i) it parameterizes the traditional static index with a pre-training model, which converts the document semantic mapping into a dynamic and updatable process; ii) with separate document identifiers, it captures both the term-level and document-level information for each document. Extensive experiments conducted on the public search benchmark MS MARCO verify the effectiveness and potential of our proposed new paradigm for information retrieval.
Tiny Autoscalers for Tiny Workloads: Dynamic CPU Allocation for Serverless Functions
Venue: arXiv cs.DC 2022
Year: 2022

In serverless computing, applications are executed under lightweight virtualization and isolation environments, such as containers or micro virtual machines. Typically, their memory allocation is set by the user before deployment. All other resources, such as CPU, are allocated by the provider statically and proportionally to memory allocations. This contributes to either under-utilization or throttling. The former significantly impacts the provider, while the latter impacts the client. To solve this problem and accommodate both clients and providers, a solution is dynamic CPU allocation achieved through autoscaling. Autoscaling has been investigated for long-running applications using history-based techniques and prediction. However, serverless applications are short-running workloads, where such techniques are not well suited. In this paper, we investigate tiny autoscalers and how dynamic CPU allocation techniques perform for short-running serverless workloads. We experiment with Kubernetes as the underlying platform and implement using its vertical pod autoscaler several dynamic CPU rightsizing techniques. We compare these techniques using state-of-the-art serverless workloads. Our experiments show that dynamic CPU allocation for short-running serverless functions is feasible and can be achieved with lightweight algorithms that offer good performance.
Humanoid Path Planning over Rough Terrain using Traversability Assessment
Venue: arXiv cs.RO 2022
Year: 2022

We present a planning framework designed for humanoid navigation over challenging terrain. This framework is designed to plan a traversable, smooth, and collision-free path using a 2.5D height map. The planner is comprised of two stages. The first stage consists of an A* planner which reasons about traversability using terrain features. A novel cost function is presented which encodes the bipedal gait directly into the graph structure, enabling natural paths that are robust to small gaps in traversability. The second stage is an optimization framework which smooths the path while further improving traversability. The planner is tested on a variety of terrains in simulation and is combined with a footstep planner and balance controller to create an integrated navigation framework, which is demonstrated on a DRC Boston Dynamics Atlas robot.
A Method Based on Deep Learning for the Detection and Characterization of Cybersecurity Incidents in Internet of Things Devices
Venue: arXiv cs.CR 2022
Year: 2022

Given the increased growing of Internet of Things networks and their presence in critical aspects of human activities, the security of devices connected to these networks becomes critical. Machine Learning approaches are becoming prominent as enablers for security solutions in computer networks due to its capacity to process traffic information in order to detect abnormal patterns which might represent attacks targeting infrastructures. In this paper, we propose to leverage Convolutional and Recurrent Neural Networks, two artifacts that have been successfully used in contexts such as image processing for pattern recognition, for the development of a security solution to be used in the context of Internet of Things. Our results show that this approach, when evaluated with a state-of-the-art data set, achieves around 99% of accuracy in the binary classification of attacks (i.e. normal traffic vs attack traffic) and 96% for multiclass classification (recognition of different types of attacks) accuracy. These results outperform proposals available in literature, showing a promising landscape for developing security solutions for IoT infrastructures.
The Transfer Student Experience: It's A Lot Like Buying a Used Car
Venue: arXiv cs.CY 2022
Year: 2022

The experience transfer students encounter as they navigate their journeys from community college to university is similar to that of buying a used car. We demonstrate this by showing how the information asymmetry in the market for used cars also occurs in the market for transfer students, producing inefficient markets in both cases, thereby increasing the chances of adverse selection. We diagnose the underlying conditions that produce transfer inefficiencies, identifying them as a structural inequity within the system of higher education. Finally, recommendations for alleviating information asymmetry in transfer processes, that would lead to better outcomes for transfer students, are provided.